{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:EWOIFDDJKBD6TVCUY4YS4FJLSP","short_pith_number":"pith:EWOIFDDJ","schema_version":"1.0","canonical_sha256":"259c828c695047e9d454c7312e152b93e7a7ce228d0949e398743471952d0440","source":{"kind":"arxiv","id":"1811.08763","version":2},"attestation_state":"computed","paper":{"title":"Comparison of Brain Networks based on Predictive Models of Connectivity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.NC","authors_text":"Fani Deligianni, Guang-Zhong Yang, Jonathan D. Clayden","submitted_at":"2018-11-19T19:43:12Z","abstract_excerpt":"In this study we adopt predictive modelling to identify simultaneously commonalities and differences in multi-modal brain networks acquired within subjects. Typically, predictive modelling of functional connectomes from structural connectomes explores commonalities across multimodal imaging data. However, direct application of multivariate approaches such as sparse Canonical Correlation Analysis (sCCA) applies on the vectorised elements of functional connectivity across subjects and it does not guarantee that the predicted models of functional connectivity are Symmetric Positive Matrices (SPD)"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1811.08763","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.NC","submitted_at":"2018-11-19T19:43:12Z","cross_cats_sorted":[],"title_canon_sha256":"b2c8dc943a4a583d9a78a34a5c8de163fac14d6ee483798c01bcd035d3c7b487","abstract_canon_sha256":"d8b18096907dcaa0b07c065078533b215e48016ad56f20d3d7630b04b522e53f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:16:59.089125Z","signature_b64":"+R2OPxnny6r+877j8+jmLuySWQJNMFEg+CxsMt0yDbUXkL/3CqDUF8osqOpZhSDf9SSjHE1Vzqbg88KJ+1SgDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"259c828c695047e9d454c7312e152b93e7a7ce228d0949e398743471952d0440","last_reissued_at":"2026-07-05T00:16:59.088670Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:16:59.088670Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Comparison of Brain Networks based on Predictive Models of Connectivity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.NC","authors_text":"Fani Deligianni, Guang-Zhong Yang, Jonathan D. Clayden","submitted_at":"2018-11-19T19:43:12Z","abstract_excerpt":"In this study we adopt predictive modelling to identify simultaneously commonalities and differences in multi-modal brain networks acquired within subjects. Typically, predictive modelling of functional connectomes from structural connectomes explores commonalities across multimodal imaging data. However, direct application of multivariate approaches such as sparse Canonical Correlation Analysis (sCCA) applies on the vectorised elements of functional connectivity across subjects and it does not guarantee that the predicted models of functional connectivity are Symmetric Positive Matrices (SPD)"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.08763","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1811.08763/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1811.08763","created_at":"2026-07-05T00:16:59.088735+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.08763v2","created_at":"2026-07-05T00:16:59.088735+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.08763","created_at":"2026-07-05T00:16:59.088735+00:00"},{"alias_kind":"pith_short_12","alias_value":"EWOIFDDJKBD6","created_at":"2026-07-05T00:16:59.088735+00:00"},{"alias_kind":"pith_short_16","alias_value":"EWOIFDDJKBD6TVCU","created_at":"2026-07-05T00:16:59.088735+00:00"},{"alias_kind":"pith_short_8","alias_value":"EWOIFDDJ","created_at":"2026-07-05T00:16:59.088735+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EWOIFDDJKBD6TVCUY4YS4FJLSP","json":"https://pith.science/pith/EWOIFDDJKBD6TVCUY4YS4FJLSP.json","graph_json":"https://pith.science/api/pith-number/EWOIFDDJKBD6TVCUY4YS4FJLSP/graph.json","events_json":"https://pith.science/api/pith-number/EWOIFDDJKBD6TVCUY4YS4FJLSP/events.json","paper":"https://pith.science/paper/EWOIFDDJ"},"agent_actions":{"view_html":"https://pith.science/pith/EWOIFDDJKBD6TVCUY4YS4FJLSP","download_json":"https://pith.science/pith/EWOIFDDJKBD6TVCUY4YS4FJLSP.json","view_paper":"https://pith.science/paper/EWOIFDDJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.08763&json=true","fetch_graph":"https://pith.science/api/pith-number/EWOIFDDJKBD6TVCUY4YS4FJLSP/graph.json","fetch_events":"https://pith.science/api/pith-number/EWOIFDDJKBD6TVCUY4YS4FJLSP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EWOIFDDJKBD6TVCUY4YS4FJLSP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EWOIFDDJKBD6TVCUY4YS4FJLSP/action/storage_attestation","attest_author":"https://pith.science/pith/EWOIFDDJKBD6TVCUY4YS4FJLSP/action/author_attestation","sign_citation":"https://pith.science/pith/EWOIFDDJKBD6TVCUY4YS4FJLSP/action/citation_signature","submit_replication":"https://pith.science/pith/EWOIFDDJKBD6TVCUY4YS4FJLSP/action/replication_record"}},"created_at":"2026-07-05T00:16:59.088735+00:00","updated_at":"2026-07-05T00:16:59.088735+00:00"}